Overview

Dataset statistics

Number of variables53
Number of observations2870
Missing cells26657
Missing cells (%)17.5%
Total size in memory1.2 MiB
Average record size in memory424.0 B

Variable types

Unsupported9
Text27
Numeric17

Alerts

机构名称 has constant value ""Constant
机构ID has constant value ""Constant
科室名称 has constant value ""Constant
入院科室名称 has constant value ""Constant
医院项目名称 has constant value ""Constant
医保项目编码 has constant value ""Constant
医保项目名称 has constant value ""Constant
收费类别 has constant value ""Constant
收费等级 has constant value ""Constant
自付比例 has constant value ""Constant
单价 has constant value ""Constant
数量 has constant value ""Constant
金额 has constant value ""Constant
明细医保范围内费用 has constant value ""Constant
规则名称 has 2870 (100.0%) missing valuesMissing
线索名称 has 2870 (100.0%) missing valuesMissing
病案号 has 119 (4.1%) missing valuesMissing
医生姓名 has 117 (4.1%) missing valuesMissing
科室名称 has 117 (4.1%) missing valuesMissing
入院科室编码 has 117 (4.1%) missing valuesMissing
入院科室名称 has 117 (4.1%) missing valuesMissing
费用科室编码 has 117 (4.1%) missing valuesMissing
费用科室名称 has 117 (4.1%) missing valuesMissing
主诊断 has 2870 (100.0%) missing valuesMissing
其他诊断 has 2870 (100.0%) missing valuesMissing
违规数量 has 2870 (100.0%) missing valuesMissing
违规金额 has 2870 (100.0%) missing valuesMissing
医保范围内违规金额 has 2870 (100.0%) missing valuesMissing
违规比例 has 2870 (100.0%) missing valuesMissing
医保实际支付违规金额 has 2870 (100.0%) missing valuesMissing
明细唯一标识 has unique valuesUnique
规则名称 is an unsupported type, check if it needs cleaning or further analysisUnsupported
线索名称 is an unsupported type, check if it needs cleaning or further analysisUnsupported
主诊断 is an unsupported type, check if it needs cleaning or further analysisUnsupported
其他诊断 is an unsupported type, check if it needs cleaning or further analysisUnsupported
违规数量 is an unsupported type, check if it needs cleaning or further analysisUnsupported
违规金额 is an unsupported type, check if it needs cleaning or further analysisUnsupported
医保范围内违规金额 is an unsupported type, check if it needs cleaning or further analysisUnsupported
违规比例 is an unsupported type, check if it needs cleaning or further analysisUnsupported
医保实际支付违规金额 is an unsupported type, check if it needs cleaning or further analysisUnsupported
大病保险支付费用 has 2827 (98.5%) zerosZeros
个人账户支付费用 has 1819 (63.4%) zerosZeros

Reproduction

Analysis started2023-09-05 03:30:04.796901
Analysis finished2023-09-05 03:30:41.979028
Duration37.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

规则名称
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

线索名称
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

机构名称
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:42.423054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters114800
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row赣州市皮肤病医院（赣州市皮肤病研究所、赣州市麻风病康复中心、赣州市性病防治中心）
2nd row赣州市皮肤病医院（赣州市皮肤病研究所、赣州市麻风病康复中心、赣州市性病防治中心）
3rd row赣州市皮肤病医院（赣州市皮肤病研究所、赣州市麻风病康复中心、赣州市性病防治中心）
4th row赣州市皮肤病医院（赣州市皮肤病研究所、赣州市麻风病康复中心、赣州市性病防治中心）
5th row赣州市皮肤病医院（赣州市皮肤病研究所、赣州市麻风病康复中心、赣州市性病防治中心）
ValueCountFrequency (%)
赣州市皮肤病医院（赣州市皮肤病研究所、赣州市麻风病康复中心、赣州市性病防治中心） 2870
100.0%
2023-09-05T11:30:43.260101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
赣 11480
 
10.0%
市 11480
 
10.0%
病 11480
 
10.0%
州 11480
 
10.0%
皮 5740
 
5.0%
肤 5740
 
5.0%
心 5740
 
5.0%
中 5740
 
5.0%
、 5740
 
5.0%
康 2870
 
2.5%
Other values (13) 37310
32.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 103320
90.0%
Other Punctuation 5740
 
5.0%
Open Punctuation 2870
 
2.5%
Close Punctuation 2870
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
赣 11480
 
11.1%
市 11480
 
11.1%
病 11480
 
11.1%
州 11480
 
11.1%
皮 5740
 
5.6%
肤 5740
 
5.6%
心 5740
 
5.6%
中 5740
 
5.6%
康 2870
 
2.8%
治 2870
 
2.8%
Other values (10) 28700
27.8%
Other Punctuation
ValueCountFrequency (%)
、 5740
100.0%
Open Punctuation
ValueCountFrequency (%)
（ 2870
100.0%
Close Punctuation
ValueCountFrequency (%)
） 2870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 103320
90.0%
Common 11480
 
10.0%

Most frequent character per script

Han
ValueCountFrequency (%)
赣 11480
 
11.1%
市 11480
 
11.1%
病 11480
 
11.1%
州 11480
 
11.1%
皮 5740
 
5.6%
肤 5740
 
5.6%
心 5740
 
5.6%
中 5740
 
5.6%
康 2870
 
2.8%
治 2870
 
2.8%
Other values (10) 28700
27.8%
Common
ValueCountFrequency (%)
、 5740
50.0%
（ 2870
25.0%
） 2870
25.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 103320
90.0%
None 11480
 
10.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
赣 11480
 
11.1%
市 11480
 
11.1%
病 11480
 
11.1%
州 11480
 
11.1%
皮 5740
 
5.6%
肤 5740
 
5.6%
心 5740
 
5.6%
中 5740
 
5.6%
康 2870
 
2.8%
治 2870
 
2.8%
Other values (10) 28700
27.8%
None
ValueCountFrequency (%)
、 5740
50.0%
（ 2870
25.0%
） 2870
25.0%

机构ID
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:43.532117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters34440
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowH36070200014
2nd rowH36070200014
3rd rowH36070200014
4th rowH36070200014
5th rowH36070200014
ValueCountFrequency (%)
h36070200014 2870
100.0%
2023-09-05T11:30:44.099149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14350
41.7%
H 2870
 
8.3%
3 2870
 
8.3%
6 2870
 
8.3%
7 2870
 
8.3%
2 2870
 
8.3%
1 2870
 
8.3%
4 2870
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31570
91.7%
Uppercase Letter 2870
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14350
45.5%
3 2870
 
9.1%
6 2870
 
9.1%
7 2870
 
9.1%
2 2870
 
9.1%
1 2870
 
9.1%
4 2870
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
H 2870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31570
91.7%
Latin 2870
 
8.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14350
45.5%
3 2870
 
9.1%
6 2870
 
9.1%
7 2870
 
9.1%
2 2870
 
9.1%
1 2870
 
9.1%
4 2870
 
9.1%
Latin
ValueCountFrequency (%)
H 2870
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14350
41.7%
H 2870
 
8.3%
3 2870
 
8.3%
6 2870
 
8.3%
7 2870
 
8.3%
2 2870
 
8.3%
1 2870
 
8.3%
4 2870
 
8.3%

就诊ID
Real number (ℝ)

Distinct1351
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131175052.1
Minimum17521967
Maximum300647568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:44.477171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17521967
5-th percentile30136423.95
Q175170621
median125485122.5
Q3176774734
95-th percentile263395737.6
Maximum300647568
Range283125601
Interquartile range (IQR)101604113

Descriptive statistics

Standard deviation68899103.18
Coefficient of variation (CV)0.5252454797
Kurtosis-0.5463902418
Mean131175052.1
Median Absolute Deviation (MAD)51139372.5
Skewness0.4091580813
Sum3.764723997 × 1011
Variance4.74708642 × 1015
MonotonicityNot monotonic
2023-09-05T11:30:44.863193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37458218 3
 
0.1%
141155487 3
 
0.1%
80031010 3
 
0.1%
165719895 3
 
0.1%
53834599 3
 
0.1%
78626494 3
 
0.1%
300647568 3
 
0.1%
55046612 3
 
0.1%
184209317 3
 
0.1%
55062776 3
 
0.1%
Other values (1341) 2840
99.0%
ValueCountFrequency (%)
17521967 2
0.1%
20166530 2
0.1%
20392644 2
0.1%
20463995 2
0.1%
20604229 3
0.1%
ValueCountFrequency (%)
300647568 3
0.1%
300639490 2
0.1%
300626877 2
0.1%
299957245 2
0.1%
299528949 2
0.1%

病案号
Real number (ℝ)

MISSING 

Distinct1295
Distinct (%)47.1%
Missing119
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean508789.8935
Minimum3005
Maximum672901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:45.224214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3005
5-th percentile237503
Q1464651
median527001
Q3583301
95-th percentile656451
Maximum672901
Range669896
Interquartile range (IQR)118650

Descriptive statistics

Standard deviation121907.5798
Coefficient of variation (CV)0.2396029901
Kurtosis4.750179662
Mean508789.8935
Median Absolute Deviation (MAD)59500
Skewness-1.895333293
Sum1399680997
Variance1.486145802 × 1010
MonotonicityNot monotonic
2023-09-05T11:30:45.609236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444401 3
 
0.1%
555601 3
 
0.1%
578101 3
 
0.1%
457201 3
 
0.1%
491401 3
 
0.1%
436301 3
 
0.1%
463401 3
 
0.1%
463001 3
 
0.1%
597801 3
 
0.1%
490201 3
 
0.1%
Other values (1285) 2721
94.8%
(Missing) 119
 
4.1%
ValueCountFrequency (%)
3005 2
0.1%
3202 2
0.1%
4002 3
0.1%
8202 2
0.1%
9404 2
0.1%
ValueCountFrequency (%)
672901 2
0.1%
672601 2
0.1%
672501 3
0.1%
672401 2
0.1%
672201 2
0.1%

医生姓名
Text

MISSING 

Distinct9
Distinct (%)0.3%
Missing117
Missing (%)4.1%
Memory size22.5 KiB
2023-09-05T11:30:45.956255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.593897566
Min length2

Characters and Unicode

Total characters7141
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row王智达
2nd row王智达
3rd row王智达
4th row王智达
5th row焦磊
ValueCountFrequency (%)
冯媛 437
15.9%
曾艳 417
15.1%
李志超 411
14.9%
叶小霞 394
14.3%
王智达 269
9.8%
焦磊 264
9.6%
陈繁斌 231
8.4%
张杨琳 169
 
6.1%
宋燕飞 161
 
5.8%
2023-09-05T11:30:46.604293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
冯 437
 
6.1%
媛 437
 
6.1%
曾 417
 
5.8%
艳 417
 
5.8%
李 411
 
5.8%
志 411
 
5.8%
超 411
 
5.8%
叶 394
 
5.5%
小 394
 
5.5%
霞 394
 
5.5%
Other values (14) 3018
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7141
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
冯 437
 
6.1%
媛 437
 
6.1%
曾 417
 
5.8%
艳 417
 
5.8%
李 411
 
5.8%
志 411
 
5.8%
超 411
 
5.8%
叶 394
 
5.5%
小 394
 
5.5%
霞 394
 
5.5%
Other values (14) 3018
42.3%

Most occurring scripts

ValueCountFrequency (%)
Han 7141
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
冯 437
 
6.1%
媛 437
 
6.1%
曾 417
 
5.8%
艳 417
 
5.8%
李 411
 
5.8%
志 411
 
5.8%
超 411
 
5.8%
叶 394
 
5.5%
小 394
 
5.5%
霞 394
 
5.5%
Other values (14) 3018
42.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 7141
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
冯 437
 
6.1%
媛 437
 
6.1%
曾 417
 
5.8%
艳 417
 
5.8%
李 411
 
5.8%
志 411
 
5.8%
超 411
 
5.8%
叶 394
 
5.5%
小 394
 
5.5%
霞 394
 
5.5%
Other values (14) 3018
42.3%

科室名称
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing117
Missing (%)4.1%
Memory size22.5 KiB
2023-09-05T11:30:46.866307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row皮肤科
2nd row皮肤科
3rd row皮肤科
4th row皮肤科
5th row皮肤科
ValueCountFrequency (%)
皮肤科 2753
100.0%
2023-09-05T11:30:47.395338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
皮 2753
33.3%
肤 2753
33.3%
科 2753
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8259
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
皮 2753
33.3%
肤 2753
33.3%
科 2753
33.3%

Most occurring scripts

ValueCountFrequency (%)
Han 8259
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
皮 2753
33.3%
肤 2753
33.3%
科 2753
33.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 8259
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
皮 2753
33.3%
肤 2753
33.3%
科 2753
33.3%

就诊类型
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:47.671354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters11480
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row普通住院
2nd row普通住院
3rd row普通住院
4th row普通住院
5th row普通住院
ValueCountFrequency (%)
普通住院 2868
99.9%
普通门诊 2
 
0.1%
2023-09-05T11:30:48.247387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
普 2870
25.0%
通 2870
25.0%
住 2868
25.0%
院 2868
25.0%
门 2
 
< 0.1%
诊 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11480
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
普 2870
25.0%
通 2870
25.0%
住 2868
25.0%
院 2868
25.0%
门 2
 
< 0.1%
诊 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Han 11480
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
普 2870
25.0%
通 2870
25.0%
住 2868
25.0%
院 2868
25.0%
门 2
 
< 0.1%
诊 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 11480
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
普 2870
25.0%
通 2870
25.0%
住 2868
25.0%
院 2868
25.0%
门 2
 
< 0.1%
诊 2
 
< 0.1%

参保地
Real number (ℝ)

Distinct36
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360802.0122
Minimum360103
Maximum369900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:48.579406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum360103
5-th percentile360702
Q1360703
median360727
Q3360740
95-th percentile360799
Maximum369900
Range9797
Interquartile range (IQR)37

Descriptive statistics

Standard deviation801.6657372
Coefficient of variation (CV)0.002221899297
Kurtosis124.4689016
Mean360802.0122
Median Absolute Deviation (MAD)23
Skewness11.21334794
Sum1035501775
Variance642667.9542
MonotonicityNot monotonic
2023-09-05T11:30:48.922425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
360702 528
18.4%
360799 357
12.4%
360704 338
11.8%
360731 204
 
7.1%
360703 203
 
7.1%
360740 176
 
6.1%
360730 131
 
4.6%
360781 100
 
3.5%
360741 95
 
3.3%
360733 76
 
2.6%
Other values (26) 662
23.1%
ValueCountFrequency (%)
360103 2
 
0.1%
360104 2
 
0.1%
360199 7
0.2%
360299 2
 
0.1%
360543 2
 
0.1%
ValueCountFrequency (%)
369900 22
0.8%
361181 2
 
0.1%
361128 2
 
0.1%
361099 2
 
0.1%
361028 2
 
0.1%

医保类型
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:49.251444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.419512195
Min length8

Characters and Unicode

Total characters27034
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row城乡居民基本医疗保险
2nd row城乡居民基本医疗保险
3rd row城乡居民基本医疗保险
4th row城乡居民基本医疗保险
5th row城乡居民基本医疗保险
ValueCountFrequency (%)
城乡居民基本医疗保险 2037
71.0%
职工基本医疗保险 833
29.0%
2023-09-05T11:30:49.909482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
基 2870
10.6%
本 2870
10.6%
医 2870
10.6%
疗 2870
10.6%
保 2870
10.6%
险 2870
10.6%
城 2037
7.5%
乡 2037
7.5%
居 2037
7.5%
民 2037
7.5%
Other values (2) 1666
6.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27034
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
基 2870
10.6%
本 2870
10.6%
医 2870
10.6%
疗 2870
10.6%
保 2870
10.6%
险 2870
10.6%
城 2037
7.5%
乡 2037
7.5%
居 2037
7.5%
民 2037
7.5%
Other values (2) 1666
6.2%

Most occurring scripts

ValueCountFrequency (%)
Han 27034
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
基 2870
10.6%
本 2870
10.6%
医 2870
10.6%
疗 2870
10.6%
保 2870
10.6%
险 2870
10.6%
城 2037
7.5%
乡 2037
7.5%
居 2037
7.5%
民 2037
7.5%
Other values (2) 1666
6.2%

Most occurring blocks

ValueCountFrequency (%)
CJK 27034
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
基 2870
10.6%
本 2870
10.6%
医 2870
10.6%
疗 2870
10.6%
保 2870
10.6%
险 2870
10.6%
城 2037
7.5%
乡 2037
7.5%
居 2037
7.5%
民 2037
7.5%
Other values (2) 1666
6.2%

患者id
Text

Distinct1334
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:50.322505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters71750
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3600001700000008116186447
2nd row3600001700000008116186447
3rd row3600001700000008116191243
4th row3600001700000008116191243
5th row3600001700000002316815478
ValueCountFrequency (%)
3600001700000000100262305 5
 
0.2%
3600001700000003111765233 4
 
0.1%
3600001700000002813827314 4
 
0.1%
3600001700000000100006469 4
 
0.1%
3600001700000000215259130 4
 
0.1%
3600001700000000100449430 4
 
0.1%
3600001700000000211493820 4
 
0.1%
3600001700000008214797675 4
 
0.1%
3600001700000002114494005 4
 
0.1%
3600001700000002001175678 4
 
0.1%
Other values (1324) 2829
98.6%
2023-09-05T11:30:51.221557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36813
51.3%
1 7815
 
10.9%
3 5483
 
7.6%
6 4982
 
6.9%
7 4433
 
6.2%
2 3589
 
5.0%
8 2276
 
3.2%
4 2220
 
3.1%
5 2108
 
2.9%
9 2031
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36813
51.3%
1 7815
 
10.9%
3 5483
 
7.6%
6 4982
 
6.9%
7 4433
 
6.2%
2 3589
 
5.0%
8 2276
 
3.2%
4 2220
 
3.1%
5 2108
 
2.9%
9 2031
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 71750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36813
51.3%
1 7815
 
10.9%
3 5483
 
7.6%
6 4982
 
6.9%
7 4433
 
6.2%
2 3589
 
5.0%
8 2276
 
3.2%
4 2220
 
3.1%
5 2108
 
2.9%
9 2031
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36813
51.3%
1 7815
 
10.9%
3 5483
 
7.6%
6 4982
 
6.9%
7 4433
 
6.2%
2 3589
 
5.0%
8 2276
 
3.2%
4 2220
 
3.1%
5 2108
 
2.9%
9 2031
 
2.8%

患者名称
Text

Distinct1324
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:51.835592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.929616725
Min length2

Characters and Unicode

Total characters8408
Distinct characters690
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row梁玉兰
2nd row梁玉兰
3rd row钟翠英
4th row钟翠英
5th row刘发忠
ValueCountFrequency (%)
张水香 6
 
0.2%
王瑞兰 5
 
0.2%
许红梅 5
 
0.2%
张金秀 4
 
0.1%
陈建高 4
 
0.1%
薛玉沅 4
 
0.1%
杨广诚 4
 
0.1%
杨倩 4
 
0.1%
张桂香 4
 
0.1%
刘秀英 4
 
0.1%
Other values (1314) 2826
98.5%
2023-09-05T11:30:52.789646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
刘 266
 
3.2%
英 210
 
2.5%
秀 182
 
2.2%
华 161
 
1.9%
黄 161
 
1.9%
兰 151
 
1.8%
李 147
 
1.7%
钟 147
 
1.7%
陈 144
 
1.7%
谢 122
 
1.5%
Other values (680) 6717
79.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8408
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
刘 266
 
3.2%
英 210
 
2.5%
秀 182
 
2.2%
华 161
 
1.9%
黄 161
 
1.9%
兰 151
 
1.8%
李 147
 
1.7%
钟 147
 
1.7%
陈 144
 
1.7%
谢 122
 
1.5%
Other values (680) 6717
79.9%

Most occurring scripts

ValueCountFrequency (%)
Han 8408
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
刘 266
 
3.2%
英 210
 
2.5%
秀 182
 
2.2%
华 161
 
1.9%
黄 161
 
1.9%
兰 151
 
1.8%
李 147
 
1.7%
钟 147
 
1.7%
陈 144
 
1.7%
谢 122
 
1.5%
Other values (680) 6717
79.9%

Most occurring blocks

ValueCountFrequency (%)
CJK 8408
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
刘 266
 
3.2%
英 210
 
2.5%
秀 182
 
2.2%
华 161
 
1.9%
黄 161
 
1.9%
兰 151
 
1.8%
李 147
 
1.7%
钟 147
 
1.7%
陈 144
 
1.7%
谢 122
 
1.5%
Other values (680) 6717
79.9%

性别
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:53.050661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2870
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row女
2nd row女
3rd row女
4th row女
5th row男
ValueCountFrequency (%)
男 1531
53.3%
女 1339
46.7%
2023-09-05T11:30:53.571691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
男 1531
53.3%
女 1339
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2870
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
男 1531
53.3%
女 1339
46.7%

Most occurring scripts

ValueCountFrequency (%)
Han 2870
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
男 1531
53.3%
女 1339
46.7%

Most occurring blocks

ValueCountFrequency (%)
CJK 2870
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
男 1531
53.3%
女 1339
46.7%

年龄
Real number (ℝ)

Distinct88
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.66236934
Minimum6
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:53.905710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q147
median60
Q370
95-th percentile82
Maximum94
Range88
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.30465084
Coefficient of variation (CV)0.3230477486
Kurtosis-0.206942158
Mean56.66236934
Median Absolute Deviation (MAD)11
Skewness-0.6486186867
Sum162621
Variance335.0602426
MonotonicityNot monotonic
2023-09-05T11:30:54.251730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 94
 
3.3%
65 92
 
3.2%
67 86
 
3.0%
68 85
 
3.0%
72 80
 
2.8%
73 79
 
2.8%
70 77
 
2.7%
69 74
 
2.6%
71 70
 
2.4%
64 69
 
2.4%
Other values (78) 2064
71.9%
ValueCountFrequency (%)
6 4
0.1%
7 2
0.1%
8 4
0.1%
9 2
0.1%
10 2
0.1%
ValueCountFrequency (%)
94 4
0.1%
93 2
 
0.1%
92 4
0.1%
91 2
 
0.1%
90 9
0.3%

患者归属
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:54.486743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5740
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row本地
2nd row本地
3rd row本地
4th row本地
5th row本地
ValueCountFrequency (%)
本地 2753
95.9%
异地 117
 
4.1%
2023-09-05T11:30:55.004773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
地 2870
50.0%
本 2753
48.0%
异 117
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5740
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
地 2870
50.0%
本 2753
48.0%
异 117
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Han 5740
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
地 2870
50.0%
本 2753
48.0%
异 117
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 5740
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
地 2870
50.0%
本 2753
48.0%
异 117
 
2.0%

就诊日期
Text

Distinct319
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:55.523803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters28700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-19
2nd row2022-04-19
3rd row2022-04-17
4th row2022-04-17
5th row2022-04-19
ValueCountFrequency (%)
2022-06-06 34
 
1.2%
2022-05-25 26
 
0.9%
2022-07-03 24
 
0.8%
2022-03-02 24
 
0.8%
2022-03-07 23
 
0.8%
2022-05-22 23
 
0.8%
2022-04-22 23
 
0.8%
2022-04-06 23
 
0.8%
2022-03-09 23
 
0.8%
2022-07-25 22
 
0.8%
Other values (309) 2625
91.5%
2023-09-05T11:30:56.370851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10154
35.4%
0 6609
23.0%
- 5740
20.0%
1 2052
 
7.1%
3 668
 
2.3%
6 660
 
2.3%
5 649
 
2.3%
4 649
 
2.3%
7 642
 
2.2%
8 569
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22960
80.0%
Dash Punctuation 5740
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10154
44.2%
0 6609
28.8%
1 2052
 
8.9%
3 668
 
2.9%
6 660
 
2.9%
5 649
 
2.8%
4 649
 
2.8%
7 642
 
2.8%
8 569
 
2.5%
9 308
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 5740
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10154
35.4%
0 6609
23.0%
- 5740
20.0%
1 2052
 
7.1%
3 668
 
2.3%
6 660
 
2.3%
5 649
 
2.3%
4 649
 
2.3%
7 642
 
2.2%
8 569
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10154
35.4%
0 6609
23.0%
- 5740
20.0%
1 2052
 
7.1%
3 668
 
2.3%
6 660
 
2.3%
5 649
 
2.3%
4 649
 
2.3%
7 642
 
2.2%
8 569
 
2.0%

出院日期
Text

Distinct320
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:56.969885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters28700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-26
2nd row2022-04-26
3rd row2022-04-22
4th row2022-04-22
5th row2022-04-25
ValueCountFrequency (%)
2022-05-31 28
 
1.0%
2022-01-26 27
 
0.9%
2022-05-12 27
 
0.9%
2022-06-11 25
 
0.9%
2022-04-25 24
 
0.8%
2022-05-28 23
 
0.8%
2022-06-29 22
 
0.8%
2022-01-12 22
 
0.8%
2022-07-09 20
 
0.7%
2022-04-19 20
 
0.7%
Other values (310) 2632
91.7%
2023-09-05T11:30:57.830935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10194
35.5%
0 6467
22.5%
- 5740
20.0%
1 2176
 
7.6%
3 721
 
2.5%
6 662
 
2.3%
5 600
 
2.1%
7 582
 
2.0%
4 578
 
2.0%
8 552
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22960
80.0%
Dash Punctuation 5740
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10194
44.4%
0 6467
28.2%
1 2176
 
9.5%
3 721
 
3.1%
6 662
 
2.9%
5 600
 
2.6%
7 582
 
2.5%
4 578
 
2.5%
8 552
 
2.4%
9 428
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 5740
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10194
35.5%
0 6467
22.5%
- 5740
20.0%
1 2176
 
7.6%
3 721
 
2.5%
6 662
 
2.3%
5 600
 
2.1%
7 582
 
2.0%
4 578
 
2.0%
8 552
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10194
35.5%
0 6467
22.5%
- 5740
20.0%
1 2176
 
7.6%
3 721
 
2.5%
6 662
 
2.3%
5 600
 
2.1%
7 582
 
2.0%
4 578
 
2.0%
8 552
 
1.9%

住院天数
Real number (ℝ)

Distinct30
Distinct (%)1.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.380055788
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:58.164954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median7
Q38
95-th percentile14
Maximum50
Range49
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.59842131
Coefficient of variation (CV)0.4875872775
Kurtosis25.82963116
Mean7.380055788
Median Absolute Deviation (MAD)1
Skewness3.620075639
Sum21166
Variance12.94863592
MonotonicityNot monotonic
2023-09-05T11:30:58.482972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6 721
25.1%
7 496
17.3%
5 413
14.4%
8 289
10.1%
4 203
 
7.1%
9 193
 
6.7%
10 145
 
5.1%
11 78
 
2.7%
3 65
 
2.3%
13 46
 
1.6%
Other values (20) 219
 
7.6%
ValueCountFrequency (%)
1 8
 
0.3%
2 17
 
0.6%
3 65
 
2.3%
4 203
7.1%
5 413
14.4%
ValueCountFrequency (%)
50 2
0.1%
41 2
0.1%
37 2
0.1%
31 2
0.1%
26 2
0.1%

结算日期
Text

Distinct320
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:30:59.050004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters28700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-26
2nd row2022-04-26
3rd row2022-04-22
4th row2022-04-22
5th row2022-04-25
ValueCountFrequency (%)
2022-01-26 29
 
1.0%
2022-05-31 28
 
1.0%
2022-06-11 25
 
0.9%
2022-05-12 25
 
0.9%
2022-04-25 24
 
0.8%
2022-05-28 23
 
0.8%
2022-07-09 22
 
0.8%
2022-06-29 22
 
0.8%
2022-01-12 22
 
0.8%
2022-04-21 19
 
0.7%
Other values (310) 2631
91.7%
2023-09-05T11:30:59.907053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10198
35.5%
0 6471
22.5%
- 5740
20.0%
1 2172
 
7.6%
3 722
 
2.5%
6 662
 
2.3%
5 594
 
2.1%
7 588
 
2.0%
4 581
 
2.0%
8 546
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22960
80.0%
Dash Punctuation 5740
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10198
44.4%
0 6471
28.2%
1 2172
 
9.5%
3 722
 
3.1%
6 662
 
2.9%
5 594
 
2.6%
7 588
 
2.6%
4 581
 
2.5%
8 546
 
2.4%
9 426
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 5740
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10198
35.5%
0 6471
22.5%
- 5740
20.0%
1 2172
 
7.6%
3 722
 
2.5%
6 662
 
2.3%
5 594
 
2.1%
7 588
 
2.0%
4 581
 
2.0%
8 546
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10198
35.5%
0 6471
22.5%
- 5740
20.0%
1 2172
 
7.6%
3 722
 
2.5%
6 662
 
2.3%
5 594
 
2.1%
7 588
 
2.0%
4 581
 
2.0%
8 546
 
1.9%

入院科室编码
Text

MISSING 

Distinct2
Distinct (%)0.1%
Missing117
Missing (%)4.1%
Memory size22.5 KiB
2023-09-05T11:31:00.149067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.00072648
Min length3

Characters and Unicode

Total characters8261
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA13
2nd rowA13
3rd rowA13
4th rowA13
5th rowA13
ValueCountFrequency (%)
a13 2751
99.9%
3100 2
 
0.1%
2023-09-05T11:31:00.655096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2753
33.3%
3 2753
33.3%
A 2751
33.3%
0 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5510
66.7%
Uppercase Letter 2751
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2753
50.0%
3 2753
50.0%
0 4
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5510
66.7%
Latin 2751
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2753
50.0%
3 2753
50.0%
0 4
 
0.1%
Latin
ValueCountFrequency (%)
A 2751
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2753
33.3%
3 2753
33.3%
A 2751
33.3%
0 4
 
< 0.1%

入院科室名称
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing117
Missing (%)4.1%
Memory size22.5 KiB
2023-09-05T11:31:00.907111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8259
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row皮肤科
2nd row皮肤科
3rd row皮肤科
4th row皮肤科
5th row皮肤科
ValueCountFrequency (%)
皮肤科 2753
100.0%
2023-09-05T11:31:01.424140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
皮 2753
33.3%
肤 2753
33.3%
科 2753
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8259
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
皮 2753
33.3%
肤 2753
33.3%
科 2753
33.3%

Most occurring scripts

ValueCountFrequency (%)
Han 8259
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
皮 2753
33.3%
肤 2753
33.3%
科 2753
33.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 8259
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
皮 2753
33.3%
肤 2753
33.3%
科 2753
33.3%

费用科室编码
Text

MISSING 

Distinct2
Distinct (%)0.1%
Missing117
Missing (%)4.1%
Memory size22.5 KiB
2023-09-05T11:31:01.626152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.00072648
Min length3

Characters and Unicode

Total characters8261
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA13
2nd rowA13
3rd rowA13
4th rowA13
5th rowA13
ValueCountFrequency (%)
a13 2751
99.9%
3108 2
 
0.1%
2023-09-05T11:31:02.142181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2753
33.3%
3 2753
33.3%
A 2751
33.3%
0 2
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5510
66.7%
Uppercase Letter 2751
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2753
50.0%
3 2753
50.0%
0 2
 
< 0.1%
8 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 2751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5510
66.7%
Latin 2751
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2753
50.0%
3 2753
50.0%
0 2
 
< 0.1%
8 2
 
< 0.1%
Latin
ValueCountFrequency (%)
A 2751
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2753
33.3%
3 2753
33.3%
A 2751
33.3%
0 2
 
< 0.1%
8 2
 
< 0.1%

费用科室名称
Text

MISSING 

Distinct2
Distinct (%)0.1%
Missing117
Missing (%)4.1%
Memory size22.5 KiB
2023-09-05T11:31:02.386195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8259
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row皮肤科
2nd row皮肤科
3rd row皮肤科
4th row皮肤科
5th row皮肤科
ValueCountFrequency (%)
皮肤科 2751
99.9%
中医科 2
 
0.1%
2023-09-05T11:31:02.930226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
科 2753
33.3%
皮 2751
33.3%
肤 2751
33.3%
中 2
 
< 0.1%
医 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8259
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
科 2753
33.3%
皮 2751
33.3%
肤 2751
33.3%
中 2
 
< 0.1%
医 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Han 8259
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
科 2753
33.3%
皮 2751
33.3%
肤 2751
33.3%
中 2
 
< 0.1%
医 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 8259
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
科 2753
33.3%
皮 2751
33.3%
肤 2751
33.3%
中 2
 
< 0.1%
医 2
 
< 0.1%

出院科室编码
Text

Distinct2
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Memory size22.5 KiB
2023-09-05T11:31:03.144239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.040794979
Min length3

Characters and Unicode

Total characters8721
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA13
2nd rowA13
3rd rowA13
4th rowA13
5th rowA13
ValueCountFrequency (%)
a13 2751
95.9%
d001 117
 
4.1%
2023-09-05T11:31:03.644267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2868
32.9%
A 2751
31.5%
3 2751
31.5%
0 234
 
2.7%
D 117
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5853
67.1%
Uppercase Letter 2868
32.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2868
49.0%
3 2751
47.0%
0 234
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
A 2751
95.9%
D 117
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5853
67.1%
Latin 2868
32.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2868
49.0%
3 2751
47.0%
0 234
 
4.0%
Latin
ValueCountFrequency (%)
A 2751
95.9%
D 117
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2868
32.9%
A 2751
31.5%
3 2751
31.5%
0 234
 
2.7%
D 117
 
1.3%

出院科室名称
Text

Distinct2
Distinct (%)0.1%
Missing2
Missing (%)0.1%
Memory size22.5 KiB
2023-09-05T11:31:03.895282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.040794979
Min length3

Characters and Unicode

Total characters8721
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row皮肤科
2nd row皮肤科
3rd row皮肤科
4th row皮肤科
5th row皮肤科
ValueCountFrequency (%)
皮肤科 2751
95.9%
异地科室 117
 
4.1%
2023-09-05T11:31:04.450313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
科 2868
32.9%
皮 2751
31.5%
肤 2751
31.5%
异 117
 
1.3%
地 117
 
1.3%
室 117
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8721
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
科 2868
32.9%
皮 2751
31.5%
肤 2751
31.5%
异 117
 
1.3%
地 117
 
1.3%
室 117
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Han 8721
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
科 2868
32.9%
皮 2751
31.5%
肤 2751
31.5%
异 117
 
1.3%
地 117
 
1.3%
室 117
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 8721
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
科 2868
32.9%
皮 2751
31.5%
肤 2751
31.5%
异 117
 
1.3%
地 117
 
1.3%
室 117
 
1.3%

主诊断
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

其他诊断
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

医疗总费用
Real number (ℝ)

Distinct1350
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5835.003254
Minimum0
Maximum23202.59
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:04.772332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3062.05
Q14377.02
median5458.9
Q36819.8075
95-th percentile10068.451
Maximum23202.59
Range23202.59
Interquartile range (IQR)2442.7875

Descriptive statistics

Standard deviation2282.206188
Coefficient of variation (CV)0.3911233788
Kurtosis5.697973773
Mean5835.003254
Median Absolute Deviation (MAD)1175.55
Skewness1.696528285
Sum16746459.34
Variance5208465.086
MonotonicityNot monotonic
2023-09-05T11:31:05.134352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4725.46 4
 
0.1%
4798.96 3
 
0.1%
5806.55 3
 
0.1%
6555.73 3
 
0.1%
5324.99 3
 
0.1%
5934.97 3
 
0.1%
6238.66 3
 
0.1%
6261.84 3
 
0.1%
3776.3 3
 
0.1%
6629.46 3
 
0.1%
Other values (1340) 2839
98.9%
ValueCountFrequency (%)
0 2
0.1%
972.4 2
0.1%
1105.2 2
0.1%
1353.18 2
0.1%
1438.23 2
0.1%
ValueCountFrequency (%)
23202.59 2
0.1%
18588.74 2
0.1%
17724.07 2
0.1%
16953.97 2
0.1%
16829.85 2
0.1%

医保范围内费用
Real number (ℝ)

Distinct1349
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4976.894467
Minimum7.5
Maximum18672.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:05.485372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile2585.246
Q13748.88
median4674.065
Q35830.55
95-th percentile8507.7195
Maximum18672.26
Range18664.76
Interquartile range (IQR)2081.67

Descriptive statistics

Standard deviation1932.444422
Coefficient of variation (CV)0.3882831824
Kurtosis4.992267906
Mean4976.894467
Median Absolute Deviation (MAD)1025.085
Skewness1.579608149
Sum14283687.12
Variance3734341.445
MonotonicityNot monotonic
2023-09-05T11:31:05.849393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4640.51 5
 
0.2%
4145.08 4
 
0.1%
4292.12 3
 
0.1%
3697.98 3
 
0.1%
5112.86 3
 
0.1%
5466.36 3
 
0.1%
5456.44 3
 
0.1%
4910.36 3
 
0.1%
3331.68 3
 
0.1%
5858.16 3
 
0.1%
Other values (1339) 2837
98.9%
ValueCountFrequency (%)
7.5 2
0.1%
673.43 2
0.1%
783.81 2
0.1%
909.73 2
0.1%
1187.78 2
0.1%
ValueCountFrequency (%)
18672.26 2
0.1%
15767.15 2
0.1%
14167.82 2
0.1%
13900.77 2
0.1%
13896.18 2
0.1%

统筹基金支付金额
Real number (ℝ)

Distinct1349
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3787.184422
Minimum0
Maximum14617.81
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:06.216414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1750.61
Q12739.5
median3515.875
Q34496.69
95-th percentile6766.68
Maximum14617.81
Range14617.81
Interquartile range (IQR)1757.19

Descriptive statistics

Standard deviation1633.982324
Coefficient of variation (CV)0.4314504239
Kurtosis4.699511227
Mean3787.184422
Median Absolute Deviation (MAD)875.315
Skewness1.535696874
Sum10869219.29
Variance2669898.235
MonotonicityNot monotonic
2023-09-05T11:31:06.568434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.2%
2996.06 4
 
0.1%
3113.7 3
 
0.1%
3515.42 3
 
0.1%
3770.29 3
 
0.1%
4053.09 3
 
0.1%
4045.15 3
 
0.1%
2605.15 3
 
0.1%
4366.53 3
 
0.1%
3608.29 3
 
0.1%
Other values (1339) 2837
98.9%
ValueCountFrequency (%)
0 5
0.2%
6 2
 
0.1%
13 2
 
0.1%
110.29 2
 
0.1%
376.95 2
 
0.1%
ValueCountFrequency (%)
14617.81 2
0.1%
12784.49 2
0.1%
12100.62 2
0.1%
11483.29 2
0.1%
11189.4 2
0.1%

基金支付总额
Real number (ℝ)

Distinct1351
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3875.408728
Minimum0
Maximum14617.81
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:06.947456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1755.3105
Q12779.3
median3584.38
Q34613.105
95-th percentile6868.1
Maximum14617.81
Range14617.81
Interquartile range (IQR)1833.805

Descriptive statistics

Standard deviation1726.758957
Coefficient of variation (CV)0.4455682169
Kurtosis5.763790656
Mean3875.408728
Median Absolute Deviation (MAD)892.52
Skewness1.748338635
Sum11122423.05
Variance2981696.495
MonotonicityNot monotonic
2023-09-05T11:31:07.484487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3089.7 3
 
0.1%
3268.53 3
 
0.1%
5010.69 3
 
0.1%
3233.54 3
 
0.1%
5169.74 3
 
0.1%
4240.33 3
 
0.1%
2734.42 3
 
0.1%
3248.31 3
 
0.1%
4408.97 3
 
0.1%
3247.69 3
 
0.1%
Other values (1341) 2840
99.0%
ValueCountFrequency (%)
0 2
0.1%
13 2
0.1%
110.29 2
0.1%
376.95 2
0.1%
630.22 2
0.1%
ValueCountFrequency (%)
14617.81 2
0.1%
14153.93 2
0.1%
13868.57 2
0.1%
13537.09 2
0.1%
12784.49 2
0.1%

大病保险支付费用
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.88710453
Minimum0
Maximum4408.97
Zeros2827
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:07.812505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4408.97
Range4408.97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation194.9035383
Coefficient of variation (CV)10.89631572
Kurtosis311.8193936
Mean17.88710453
Median Absolute Deviation (MAD)0
Skewness16.10128552
Sum51335.99
Variance37987.38923
MonotonicityNot monotonic
2023-09-05T11:31:08.100522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 2827
98.5%
1057.54 3
 
0.1%
4408.97 3
 
0.1%
972.94 3
 
0.1%
539.63 2
 
0.1%
1206.99 2
 
0.1%
152.52 2
 
0.1%
121.37 2
 
0.1%
332.89 2
 
0.1%
773.56 2
 
0.1%
Other values (11) 22
 
0.8%
ValueCountFrequency (%)
0 2827
98.5%
121.37 2
 
0.1%
152.52 2
 
0.1%
332.89 2
 
0.1%
355.19 2
 
0.1%
ValueCountFrequency (%)
4408.97 3
0.1%
2814.11 2
0.1%
1953.17 2
0.1%
1531.64 2
0.1%
1467.38 2
0.1%

个人账户支付费用
Real number (ℝ)

ZEROS 

Distinct478
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.1781847
Minimum0
Maximum2268.01
Zeros1819
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:08.425541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3180
95-th percentile1141.88
Maximum2268.01
Range2268.01
Interquartile range (IQR)180

Descriptive statistics

Standard deviation406.4971794
Coefficient of variation (CV)1.934059808
Kurtosis3.740857461
Mean210.1781847
Median Absolute Deviation (MAD)0
Skewness2.106190428
Sum603211.39
Variance165239.9569
MonotonicityNot monotonic
2023-09-05T11:31:08.771560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1819
63.4%
90 17
 
0.6%
180 15
 
0.5%
360 4
 
0.1%
270 4
 
0.1%
275 4
 
0.1%
123 4
 
0.1%
15 4
 
0.1%
175 4
 
0.1%
95 4
 
0.1%
Other values (468) 991
34.5%
ValueCountFrequency (%)
0 1819
63.4%
0.08 2
 
0.1%
0.1 2
 
0.1%
0.3 2
 
0.1%
0.47 2
 
0.1%
ValueCountFrequency (%)
2268.01 2
0.1%
2249.12 2
0.1%
2215.54 2
0.1%
1862.36 2
0.1%
1833.64 3
0.1%

收费日期
Text

Distinct316
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:09.268589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters28700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2022-04-19
2nd row2022-04-19
3rd row2022-04-17
4th row2022-04-17
5th row2022-04-19
ValueCountFrequency (%)
2022-06-06 34
 
1.2%
2022-05-25 26
 
0.9%
2022-03-07 25
 
0.9%
2022-03-02 25
 
0.9%
2022-04-22 23
 
0.8%
2022-05-09 23
 
0.8%
2022-05-22 23
 
0.8%
2022-07-03 22
 
0.8%
2022-07-04 22
 
0.8%
2022-07-25 22
 
0.8%
Other values (306) 2625
91.5%
2023-09-05T11:31:10.074635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10159
35.4%
0 6606
23.0%
- 5740
20.0%
1 2061
 
7.2%
3 667
 
2.3%
6 662
 
2.3%
5 649
 
2.3%
4 647
 
2.3%
7 637
 
2.2%
8 568
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22960
80.0%
Dash Punctuation 5740
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10159
44.2%
0 6606
28.8%
1 2061
 
9.0%
3 667
 
2.9%
6 662
 
2.9%
5 649
 
2.8%
4 647
 
2.8%
7 637
 
2.8%
8 568
 
2.5%
9 304
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 5740
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10159
35.4%
0 6606
23.0%
- 5740
20.0%
1 2061
 
7.2%
3 667
 
2.3%
6 662
 
2.3%
5 649
 
2.3%
4 647
 
2.3%
7 637
 
2.2%
8 568
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10159
35.4%
0 6606
23.0%
- 5740
20.0%
1 2061
 
7.2%
3 667
 
2.3%
6 662
 
2.3%
5 649
 
2.3%
4 647
 
2.3%
7 637
 
2.2%
8 568
 
2.0%

医院项目编码
Text

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:10.364651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length4
Mean length4.028919861
Min length4

Characters and Unicode

Total characters11563
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3426
2nd row3426
3rd row3426
4th row3426
5th row3426
ValueCountFrequency (%)
3426 1593
55.5%
3427 592
 
20.6%
3447 471
 
16.4%
3428 122
 
4.3%
3430 69
 
2.4%
z6021 5
 
0.2%
1002523 4
 
0.1%
220301001 3
 
0.1%
jxzy200229 2
 
0.1%
22030100121 2
 
0.1%
Other values (4) 7
 
0.2%
2023-09-05T11:31:10.936684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 3320
28.7%
3 2928
25.3%
2 2344
20.3%
6 1600
13.8%
7 1063
 
9.2%
8 126
 
1.1%
0 120
 
1.0%
1 29
 
0.3%
Z 10
 
0.1%
5 6
 
0.1%
Other values (6) 17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11540
99.8%
Uppercase Letter 23
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 3320
28.8%
3 2928
25.4%
2 2344
20.3%
6 1600
13.9%
7 1063
 
9.2%
8 126
 
1.1%
0 120
 
1.0%
1 29
 
0.3%
5 6
 
0.1%
9 4
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Z 10
43.5%
J 3
 
13.0%
X 3
 
13.0%
Y 3
 
13.0%
B 2
 
8.7%
S 2
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 11540
99.8%
Latin 23
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
4 3320
28.8%
3 2928
25.4%
2 2344
20.3%
6 1600
13.9%
7 1063
 
9.2%
8 126
 
1.1%
0 120
 
1.0%
1 29
 
0.3%
5 6
 
0.1%
9 4
 
< 0.1%
Latin
ValueCountFrequency (%)
Z 10
43.5%
J 3
 
13.0%
X 3
 
13.0%
Y 3
 
13.0%
B 2
 
8.7%
S 2
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 3320
28.7%
3 2928
25.3%
2 2344
20.3%
6 1600
13.8%
7 1063
 
9.2%
8 126
 
1.1%
0 120
 
1.0%
1 29
 
0.3%
Z 10
 
0.1%
5 6
 
0.1%
Other values (6) 17
 
0.1%

医院项目名称
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:11.275704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters31570
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row彩色多普勒超声常规检查
2nd row彩色多普勒超声常规检查
3rd row彩色多普勒超声常规检查
4th row彩色多普勒超声常规检查
5th row彩色多普勒超声常规检查
ValueCountFrequency (%)
彩色多普勒超声常规检查 2870
100.0%
2023-09-05T11:31:11.884738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
彩 2870
9.1%
色 2870
9.1%
多 2870
9.1%
普 2870
9.1%
勒 2870
9.1%
超 2870
9.1%
声 2870
9.1%
常 2870
9.1%
规 2870
9.1%
检 2870
9.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31570
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
彩 2870
9.1%
色 2870
9.1%
多 2870
9.1%
普 2870
9.1%
勒 2870
9.1%
超 2870
9.1%
声 2870
9.1%
常 2870
9.1%
规 2870
9.1%
检 2870
9.1%

Most occurring scripts

ValueCountFrequency (%)
Han 31570
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
彩 2870
9.1%
色 2870
9.1%
多 2870
9.1%
普 2870
9.1%
勒 2870
9.1%
超 2870
9.1%
声 2870
9.1%
常 2870
9.1%
规 2870
9.1%
检 2870
9.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 31570
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
彩 2870
9.1%
色 2870
9.1%
多 2870
9.1%
普 2870
9.1%
勒 2870
9.1%
超 2870
9.1%
声 2870
9.1%
常 2870
9.1%
规 2870
9.1%
检 2870
9.1%

医保项目编码
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:12.192756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters71750
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row002203010010000-220301001
2nd row002203010010000-220301001
3rd row002203010010000-220301001
4th row002203010010000-220301001
5th row002203010010000-220301001
ValueCountFrequency (%)
002203010010000-220301001 2870
100.0%
2023-09-05T11:31:12.793791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 40180
56.0%
2 11480
 
16.0%
1 11480
 
16.0%
3 5740
 
8.0%
- 2870
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68880
96.0%
Dash Punctuation 2870
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40180
58.3%
2 11480
 
16.7%
1 11480
 
16.7%
3 5740
 
8.3%
Dash Punctuation
ValueCountFrequency (%)
- 2870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40180
56.0%
2 11480
 
16.0%
1 11480
 
16.0%
3 5740
 
8.0%
- 2870
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40180
56.0%
2 11480
 
16.0%
1 11480
 
16.0%
3 5740
 
8.0%
- 2870
 
4.0%

医保项目名称
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:13.130810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters31570
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row彩色多普勒超声常规检查
2nd row彩色多普勒超声常规检查
3rd row彩色多普勒超声常规检查
4th row彩色多普勒超声常规检查
5th row彩色多普勒超声常规检查
ValueCountFrequency (%)
彩色多普勒超声常规检查 2870
100.0%
2023-09-05T11:31:13.748845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
彩 2870
9.1%
色 2870
9.1%
多 2870
9.1%
普 2870
9.1%
勒 2870
9.1%
超 2870
9.1%
声 2870
9.1%
常 2870
9.1%
规 2870
9.1%
检 2870
9.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31570
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
彩 2870
9.1%
色 2870
9.1%
多 2870
9.1%
普 2870
9.1%
勒 2870
9.1%
超 2870
9.1%
声 2870
9.1%
常 2870
9.1%
规 2870
9.1%
检 2870
9.1%

Most occurring scripts

ValueCountFrequency (%)
Han 31570
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
彩 2870
9.1%
色 2870
9.1%
多 2870
9.1%
普 2870
9.1%
勒 2870
9.1%
超 2870
9.1%
声 2870
9.1%
常 2870
9.1%
规 2870
9.1%
检 2870
9.1%

Most occurring blocks

ValueCountFrequency (%)
CJK 31570
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
彩 2870
9.1%
色 2870
9.1%
多 2870
9.1%
普 2870
9.1%
勒 2870
9.1%
超 2870
9.1%
声 2870
9.1%
常 2870
9.1%
规 2870
9.1%
检 2870
9.1%

收费类别
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:13.980859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8610
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row检查费
2nd row检查费
3rd row检查费
4th row检查费
5th row检查费
ValueCountFrequency (%)
检查费 2870
100.0%
2023-09-05T11:31:14.510889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
检 2870
33.3%
查 2870
33.3%
费 2870
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8610
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
检 2870
33.3%
查 2870
33.3%
费 2870
33.3%

Most occurring scripts

ValueCountFrequency (%)
Han 8610
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
检 2870
33.3%
查 2870
33.3%
费 2870
33.3%

Most occurring blocks

ValueCountFrequency (%)
CJK 8610
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
检 2870
33.3%
查 2870
33.3%
费 2870
33.3%

收费等级
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:14.720901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters5740
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row乙类
2nd row乙类
3rd row乙类
4th row乙类
5th row乙类
ValueCountFrequency (%)
乙类 2870
100.0%
2023-09-05T11:31:15.219929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
乙 2870
50.0%
类 2870
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5740
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
乙 2870
50.0%
类 2870
50.0%

Most occurring scripts

ValueCountFrequency (%)
Han 5740
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
乙 2870
50.0%
类 2870
50.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 5740
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
乙 2870
50.0%
类 2870
50.0%

自付比例
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15
Minimum0.15
Maximum0.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:15.495945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.15
Q10.15
median0.15
Q30.15
95-th percentile0.15
Maximum0.15
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.776041235 × 10-17
Coefficient of variation (CV)1.850694156 × 10-16
Kurtosis0
Mean0.15
Median Absolute Deviation (MAD)0
Skewness0
Sum430.5
Variance7.706404936 × 10-34
MonotonicityIncreasing
2023-09-05T11:31:15.752960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0.15 2870
100.0%
ValueCountFrequency (%)
0.15 2870
100.0%
ValueCountFrequency (%)
0.15 2870
100.0%

单价
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70
Minimum70
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:16.000974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile70
Q170
median70
Q370
95-th percentile70
Maximum70
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean70
Median Absolute Deviation (MAD)0
Skewness0
Sum200900
Variance0
MonotonicityIncreasing
2023-09-05T11:31:16.263989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
70 2870
100.0%
ValueCountFrequency (%)
70 2870
100.0%
ValueCountFrequency (%)
70 2870
100.0%

数量
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:16.508003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean1
Median Absolute Deviation (MAD)0
Skewness0
Sum2870
Variance0
MonotonicityIncreasing
2023-09-05T11:31:16.769018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1 2870
100.0%
ValueCountFrequency (%)
1 2870
100.0%
ValueCountFrequency (%)
1 2870
100.0%

金额
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70
Minimum70
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:17.029033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile70
Q170
median70
Q370
95-th percentile70
Maximum70
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean70
Median Absolute Deviation (MAD)0
Skewness0
Sum200900
Variance0
MonotonicityIncreasing
2023-09-05T11:31:17.285047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
70 2870
100.0%
ValueCountFrequency (%)
70 2870
100.0%
ValueCountFrequency (%)
70 2870
100.0%

明细医保范围内费用
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.5
Minimum59.5
Maximum59.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:17.538062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59.5
5-th percentile59.5
Q159.5
median59.5
Q359.5
95-th percentile59.5
Maximum59.5
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean59.5
Median Absolute Deviation (MAD)0
Skewness0
Sum170765
Variance0
MonotonicityIncreasing
2023-09-05T11:31:17.807077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
59.5 2870
100.0%
ValueCountFrequency (%)
59.5 2870
100.0%
ValueCountFrequency (%)
59.5 2870
100.0%

违规数量
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

违规金额
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

医保范围内违规金额
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

违规比例
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

医保实际支付违规金额
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2870
Missing (%)100.0%
Memory size22.5 KiB

明细唯一标识
Text

UNIQUE 

Distinct2870
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:18.220101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.63902439
Min length16

Characters and Unicode

Total characters47754
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2870 ?
Unique (%)100.0%

Sample

1st row4137961_100316432
2nd row4137960_100316432
3rd row4129312_100317469
4th row4129311_100317469
5th row4135796_100318826
ValueCountFrequency (%)
4137961_100316432 1
 
< 0.1%
4147976_102380041 1
 
< 0.1%
4139544_101015795 1
 
< 0.1%
4137477_100325066 1
 
< 0.1%
4129312_100317469 1
 
< 0.1%
4129311_100317469 1
 
< 0.1%
4135796_100318826 1
 
< 0.1%
4135797_100318826 1
 
< 0.1%
4136609_100323085 1
 
< 0.1%
4136608_100323085 1
 
< 0.1%
Other values (2860) 2860
99.7%
2023-09-05T11:31:18.968144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 6198
13.0%
1 5491
11.5%
3 4960
10.4%
2 4391
9.2%
0 4175
8.7%
9 4023
8.4%
7 3989
8.4%
5 3961
8.3%
6 3953
8.3%
8 3743
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44884
94.0%
Connector Punctuation 2870
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6198
13.8%
1 5491
12.2%
3 4960
11.1%
2 4391
9.8%
0 4175
9.3%
9 4023
9.0%
7 3989
8.9%
5 3961
8.8%
6 3953
8.8%
8 3743
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 2870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6198
13.0%
1 5491
11.5%
3 4960
10.4%
2 4391
9.2%
0 4175
8.7%
9 4023
8.4%
7 3989
8.4%
5 3961
8.3%
6 3953
8.3%
8 3743
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6198
13.0%
1 5491
11.5%
3 4960
10.4%
2 4391
9.2%
0 4175
8.7%
9 4023
8.4%
7 3989
8.4%
5 3961
8.3%
6 3953
8.3%
8 3743
7.8%

超量标识
Real number (ℝ)

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.580139373
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-05T11:31:19.259160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5959863268
Coefficient of variation (CV)0.3771732652
Kurtosis-0.6591177553
Mean1.580139373
Median Absolute Deviation (MAD)1
Skewness0.479498054
Sum4535
Variance0.3551997017
MonotonicityNot monotonic
2023-09-05T11:31:19.549177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
1 1365
47.6%
2 1345
46.9%
3 160
 
5.6%
ValueCountFrequency (%)
1 1365
47.6%
2 1345
46.9%
3 160
 
5.6%
ValueCountFrequency (%)
3 160
 
5.6%
2 1345
46.9%
1 1365
47.6%